The simultaneous use of multiple sources of information is known to improve the performance of identity recognition systems. For example, it was shown in UK Patent GB 2,229,305 A to Fleming that verification procedures used to identify an individual can be strengthened by the use of multiple biometric inputs. The Fleming disclosure describes an identity verification system based on collecting, through appropriate biometric tests, various user specific characteristics (e.g., face, fingerprint, and voice characteristics), and combining the results of these test through a weighted sum of each individual score.
The recognition of a person, however, is quite different from the recognition of a message conveyed by that person. Even though voice and/or handwriting inputs, for example, may be used in both cases, user identification requires only a relatively short voice pattern (such as a password), or a relatively concise handwriting pattern (such as a signature), for the sole purpose of ascertaining the identity of the user. A user identification procedure typically must compare the evidence provided by the user to a unique set of templates stored for this user. A score is assigned to the evidence and a comparison of the score to a threshold is performed. The comparison is either successful or unsuccessful (i.e., this is a binary decision).
In contrast, message recognition is a far more complex task. Message recognition generally requires long utterances or a substantial amount of handwriting, involving complex syntactic and semantic structures constructed from a large vocabulary of words. The output of a message recognizer is a sequence of words representing the message being created. A message recognition procedure typically consists of decoding the current set of input data (such as, for example, the utterance spoken for the current word or the sequence of characters handwritten for the current word) by comparing the input against a plurality of potential candidate words in the vocabulary. Each candidate word must be assigned a score, and the decoded word is determined by which score is the highest.
Of particular interest are procedures for adjusting internal parameters used by the recognition system to the characteristics of each individual user. As described in F. Jelinek, "The Development of an Experimental Discrete Dictation Recognizer", Proc. IEEE, Vol. 73, No. 11, pp. 1616-1624 (November 1985), this adjustment, called training, has the potential to improve the accuracy performance during recognition (also referred to as decoding).
Moreover, the simultaneous use of multiple sources of information is known to improve the performance of automatic message recognition systems. For example, it was demonstrated in U.S. Pat. No. 3,192,321 to Nassimbene and E. D. Petajan, "Automatic Lipreading to Enhance Speech Recognition", IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-33, No 1, pp. 40-47, (January 1985), that using lipreading information can enhance automatic speech recognition. However, the procedures used in these disclosures provide a trivial straightforward strategy and are based on a simple comparison of output words from an automatic speech recognizer and automatic lipreading recognizer.
Finally, it was shown in U.S. patent application Ser. No. 07/676,601 filed Mar. 28, 1992 now abandoned, naming J. Bellegarda and D. Kanevsky as co-inventors, and entitled "Improved Message Recognition through the Integration of Speech and Handwriting Information," which is hereby incorporated by reference in its entirety herein, that the integration of speech and handwriting information can lead to the improved recognition of a consistent message. This application addresses the problem of merging the evidence obtained from two sources during decoding, but not during training.
As sensor technology develops, it is likely that in the near future, more and more measurements from many different sources of information will be available to the automatic message recognition system. The challenge is therefore to integrate the evidence provided by these many sources to perform more accurate and robust decoding.
Thus, given the fundamental difference between identification of an individual and identification of a consistent message, which makes it clear that the same procedure cannot be used in both cases, and the demonstrated ability of multiple sources of information to improve the performance of a recognition system, it is readily apparent that a procedure for using multiple sources of information in the automatic recognition of a consistent message is needed.